Dataframe 基於索引和列名的條件填充

Question

我希望能夠在 Dataframe 上使用df.fillna() function，但根據該特定單元格的索引和列名對其應用條件。

我正在嘗試根據以下數據集創建曲棍球隊友數據的熱圖（為下面的大字典道歉）-

linemates_toi = {
    'Player 1': {'Player 2': 0.25, 'Player 3': 7.95, 'Player 4': 0.6333, 'Player 5': 9.95, 'Player 6': 0.6333, 'Player 7': 0.8, 'Player 8': 4.2667, 'Player 9': 7.8833, 'Player 10': 0.3, 'Player 11': 11.2333, 'Player 12': 3.35, 'Player 13': 0.2167},
    'Player 10': {'Player 14': 2.3, 'Player 18': 1.2667, 'Player 2': 6.8333, 'Player 4': 5.5833, 'Player 5': 0.9, 'Player 16': 6.9167, 'Player 6': 4.9667, 'Player 7': 4.15, 'Player 15': 1.0, 'Player 8': 0.3167, 'Player 17': 5.3167, 'Player 1': 0.3, 'Player 11': 1.6167, 'Player 12': 0.6833, 'Player 13': 12.7167}, 
    'Player 12': {'Player 14': 4.5333, 'Player 18': 4.3333, 'Player 2': 3.1167, 'Player 3': 1.2333, 'Player 4': 5.7333, 'Player 5': 3.5167, 'Player 16': 3.0, 'Player 6': 3.0167, 'Player 7': 2.4, 'Player 15': 2.0167, 'Player 8': 11.6667, 'Player 17': 2.2667, 'Player 9': 0.1167, 'Player 1': 3.35, 'Player 10': 0.6833, 'Player 11': 3.35}, 
    'Player 17': {'Player 14': 4.55, 'Player 18': 1.65, 'Player 2': 0.8833, 'Player 3': 2.85, 'Player 5': 0.0333, 'Player 16': 2.9167, 'Player 6': 7.8167, 'Player 7': 6.0833, 'Player 8': 3.8, 'Player 9': 2.25, 'Player 10': 5.3167, 'Player 12': 2.2667, 'Player 13': 5.7833},
    'Player 7': {'Player 18': 0.3667, 'Player 2': 0.6667, 'Player 3': 1.55, 'Player 4': 0.3333, 'Player 5': 0.15, 'Player 16': 1.2167, 'Player 6': 6.8333, 'Player 15': 0.3333, 'Player 8': 3.0667, 'Player 17': 6.0833, 'Player 9': 1.8833, 'Player 1': 0.8, 'Player 10': 4.15, 'Player 11': 1.0, 'Player 12': 2.4, 'Player 13': 4.4333}, 
    'Player 16': {'Player 14': 2.2833, 'Player 2': 8.5333, 'Player 3': 2.7, 'Player 4': 8.0167, 'Player 5': 0.45, 'Player 6': 0.4, 'Player 7': 1.2167, 'Player 8': 2.3, 'Player 17': 2.9167, 'Player 9': 2.15, 'Player 10': 6.9167, 'Player 11': 0.1333, 'Player 12': 3.0, 'Player 13': 6.5833},
    'Player 18': {'Player 14': 10.05, 'Player 2': 0.75, 'Player 3': 5.0, 'Player 4': 3.45, 'Player 5': 0.3333, 'Player 6': 0.8333, 'Player 7': 0.3667, 'Player 15': 5.2, 'Player 8': 5.8167, 'Player 17': 1.65, 'Player 9': 4.3833, 'Player 10': 1.2667, 'Player 11': 1.5, 'Player 12': 4.3333, 'Player 13': 1.5333},
    'Player 13': {'Player 14': 3.0333, 'Player 18': 1.5333, 'Player 2': 5.9167, 'Player 3': 0.7333, 'Player 4': 4.95, 'Player 5': 0.8167, 'Player 16': 6.5833, 'Player 6': 5.1333, 'Player 7': 4.4333, 'Player 15': 1.2667, 'Player 8': 0.2833, 'Player 17': 5.7833, 'Player 1': 0.2167, 'Player 10': 12.7167, 'Player 11': 1.5333},
    'Player 5': {'Player 18': 0.3333, 'Player 2': 0.8333, 'Player 3': 8.0333, 'Player 16': 0.45, 'Player 6': 0.3333, 'Player 7': 0.15, 'Player 8': 3.0167, 'Player 17': 0.0333, 'Player 9': 6.7333, 'Player 1': 9.95, 'Player 10': 0.9, 'Player 11': 11.2333, 'Player 12': 3.5167, 'Player 13': 0.8167},
    'Player 15': {'Player 14': 4.5667, 'Player 18': 5.2, 'Player 2': 0.4667, 'Player 3': 2.35, 'Player 6': 0.1667, 'Player 7': 0.3333, 'Player 8': 2.0167, 'Player 9': 2.0833, 'Player 10': 1.0, 'Player 12': 2.0167, 'Player 13': 1.2667},
    'Player 2': {'Player 18': 0.75, 'Player 3': 2.65, 'Player 4': 8.6, 'Player 5': 0.8333, 'Player 16': 8.5333, 'Player 6': 0.8333, 'Player 7': 0.6667, 'Player 15': 0.4667, 'Player 8': 2.3333, 'Player 17': 0.8833, 'Player 9': 1.9167, 'Player 1': 0.25, 'Player 10': 6.8333, 'Player 11': 1.6167, 'Player 12': 3.1167, 'Player 13': 5.9167},
    'Player 8': {'Player 14': 5.8333, 'Player 18': 5.8167, 'Player 2': 2.3333, 'Player 3': 1.1167, 'Player 4': 5.6833, 'Player 5': 3.0167, 'Player 16': 2.3, 'Player 6': 4.2667, 'Player 7': 3.0667, 'Player 15': 2.0167, 'Player 17': 3.8, 'Player 9': 1.1333, 'Player 1': 4.2667, 'Player 10': 0.3167, 'Player 11': 3.8167, 'Player 12': 11.6667, 'Player 13': 0.2833},
    'Player 4': {'Player 14': 3.2833, 'Player 18': 3.45, 'Player 2': 8.6, 'Player 3': 2.0667, 'Player 16': 8.0167, 'Player 6': 0.8333, 'Player 7': 0.3333, 'Player 8': 5.6833, 'Player 9': 1.85, 'Player 1': 0.6333, 'Player 10': 5.5833, 'Player 11': 0.85, 'Player 12': 5.7333, 'Player 13': 4.95},
    'Player 9': {'Player 14': 4.5167, 'Player 18': 4.3833, 'Player 2': 1.9167, 'Player 3': 14.35, 'Player 4': 1.85, 'Player 5': 6.7333, 'Player 16': 2.15, 'Player 6': 0.8833, 'Player 7': 1.8833, 'Player 15': 2.0833, 'Player 8': 1.1333, 'Player 17': 2.25, 'Player 1': 7.8833, 'Player 11': 9.0667, 'Player 12': 0.1167},
    'Player 14': {'Player 18': 10.05, 'Player 3': 5.7167, 'Player 4': 3.2833, 'Player 16': 2.2833, 'Player 6': 1.8833, 'Player 15': 4.5667, 'Player 8': 5.8333, 'Player 17': 4.55, 'Player 9': 4.5167, 'Player 10': 2.3, 'Player 11': 0.9833, 'Player 12': 4.5333, 'Player 13': 3.0333},
    'Player 11': {'Player 14': 0.9833, 'Player 18': 1.5, 'Player 2': 1.6167, 'Player 3': 9.7667, 'Player 4': 0.85, 'Player 5': 11.2333, 'Player 16': 0.1333, 'Player 6': 0.5, 'Player 7': 1.0, 'Player 8': 3.8167, 'Player 9': 9.0667, 'Player 1': 11.2333, 'Player 10': 1.6167, 'Player 12': 3.35, 'Player 13': 1.5333},
    'Player 6': {'Player 14': 1.8833, 'Player 18': 0.8333, 'Player 2': 0.8333, 'Player 3': 1.1333, 'Player 4': 0.8333, 'Player 5': 0.3333, 'Player 16': 0.4, 'Player 7': 6.8333, 'Player 15': 0.1667, 'Player 8': 4.2667, 'Player 17': 7.8167, 'Player 9': 0.8833, 'Player 1': 0.6333, 'Player 10': 4.9667, 'Player 11': 0.5, 'Player 12': 3.0167, 'Player 13': 5.1333},
    'Player 3': {'Player 14': 5.7167, 'Player 18': 5.0, 'Player 2': 2.65, 'Player 4': 2.0667, 'Player 5': 8.0333, 'Player 16': 2.7, 'Player 6': 1.1333, 'Player 7': 1.55, 'Player 15': 2.35, 'Player 8': 1.1167, 'Player 17': 2.85, 'Player 9': 14.35, 'Player 1': 7.95, 'Player 11': 9.7667, 'Player 12': 1.2333, 'Player 13': 0.7333}
}

df = pd.DataFrame(linemates_toi)

我現在要實現的是使用df.fillna(0)並應用條件，因此唯一被替換的NaN是索引和列名稱不匹配時，因為我希望這些單元格保持NaN以便當我plot 將它們放入熱圖中，它們在從 Matplotlib 應用的cmap中沒有任何顏色。

如果我正在編寫偽代碼，它看起來像這樣 -

df.fillna(0, df.cell.Index.Name != df.cell.Column.Name)

提前致謝！

Answer 1

使用一些廣播和NaN -masking

mask = df.index.to_numpy() == df.columns.to_numpy()[:, None]

df.fillna(0).mask(mask)

---

>>> df.head()

           Player 1  Player 10  Player 12  Player 17  Player 7  Player 16  \
Player 1        NaN     0.3000     3.3500     0.0000    0.8000     0.0000   
Player 10    0.3000        NaN     0.6833     5.3167    4.1500     6.9167   
Player 11   11.2333     1.6167     3.3500     0.0000    1.0000     0.1333   
Player 12    3.3500     0.6833        NaN     2.2667    2.4000     3.0000   
Player 13    0.2167    12.7167     0.0000     5.7833    4.4333     6.5833   

           Player 18  Player 13  Player 5  Player 15  Player 2  Player 8  \
Player 1      0.0000     0.2167    9.9500     0.0000    0.2500    4.2667   
Player 10     1.2667    12.7167    0.9000     1.0000    6.8333    0.3167   
Player 11     1.5000     1.5333   11.2333     0.0000    1.6167    3.8167   
Player 12     4.3333     0.0000    3.5167     2.0167    3.1167   11.6667   
Player 13     1.5333        NaN    0.8167     1.2667    5.9167    0.2833   

           Player 4  Player 9  Player 14  Player 11  Player 6  Player 3  
Player 1     0.6333    7.8833     0.0000    11.2333    0.6333    7.9500  
Player 10    5.5833    0.0000     2.3000     1.6167    4.9667    0.0000  
Player 11    0.8500    9.0667     0.9833        NaN    0.5000    9.7667  
Player 12    5.7333    0.1167     4.5333     3.3500    3.0167    1.2333  
Player 13    4.9500    0.0000     3.0333     1.5333    5.1333    0.7333 

Answer 2

您也可以執行以下操作：

df.fillna(0, inplace=True)
for col in df:
    df.loc[col, col] = np.nan

解釋：

1. 填充 NaN
2. 設置 NaN，其中行名等於列名

Answer 3

在每列上使用df.apply到 map 和 lambda ：

df = df.apply(lambda col: col.where((col.name == col.index) | col.notnull(), 0))

col.where(condition, value_if_false)如果condition為真，則返回col中的原始值。 否則返回value_if_false